class_docstr: MetadataWriter to write the metadata and the model asset bundle.
class_docstr_tok: ["MetadataWriter","to","write","the","metadata","and","the","model","asset","bundle","."]
docstr: Creates MetadataWriter to write the metadata for gesture recognizer.
  
  Args:
    hand_detector_model_buffer: A valid flatbuffer *with* metadata loaded from
      the TFLite hand detector model file.
    hand_landmarks_detector_model_buffer: A valid flatbuffer *with* metadata
      loaded from the TFLite hand landmarks detector model file.
    gesture_embedder_model_buffer: A valid flatbuffer *with* metadata loaded
      from the TFLite gesture embedder model file.
    canned_gesture_classifier_model_buffer: A valid flatbuffer *with* metadata
      loaded from the TFLite canned gesture classifier model file.
    custom_gesture_classifier_options: Custom gesture classifier options to
      write custom gesture classifier metadata into the TFLite file.
  
  Returns:
    An MetadataWrite object.
docstr_tok: ["Creates","MetadataWriter","to","write","the","metadata","for","gesture","recognizer",".","Args",":","hand_detector_model_buffer",":","A","valid","flatbuffer","*","with","*","metadata","loaded","from","the","TFLite","hand","detector","model","file",".","hand_landmarks_detector_model_buffer",":","A","valid","flatbuffer","*","with","*","metadata","loaded","from","the","TFLite","hand","landmarks","detector","model","file",".","gesture_embedder_model_buffer",":","A","valid","flatbuffer","*","with","*","metadata","loaded","from","the","TFLite","gesture","embedder","model","file",".","canned_gesture_classifier_model_buffer",":","A","valid","flatbuffer","*","with","*","metadata","loaded","from","the","TFLite","canned","gesture","classifier","model","file",".","custom_gesture_classifier_options",":","Custom","gesture","classifier","options","to","write","custom","gesture","classifier","metadata","into","the","TFLite","file",".","Returns",":","An","MetadataWrite","object","."]
code: def create(
      cls,
      hand_detector_model_buffer: bytearray,
      hand_landmarks_detector_model_buffer: bytearray,
      gesture_embedder_model_buffer: bytearray,
      canned_gesture_classifier_model_buffer: bytearray,
      custom_gesture_classifier_options: GestureClassifierOptions,
  ) -> "MetadataWriter":
      """Creates MetadataWriter to write the metadata for gesture recognizer.
  
      Args:
        hand_detector_model_buffer: A valid flatbuffer *with* metadata loaded from
          the TFLite hand detector model file.
        hand_landmarks_detector_model_buffer: A valid flatbuffer *with* metadata
          loaded from the TFLite hand landmarks detector model file.
        gesture_embedder_model_buffer: A valid flatbuffer *with* metadata loaded
          from the TFLite gesture embedder model file.
        canned_gesture_classifier_model_buffer: A valid flatbuffer *with* metadata
          loaded from the TFLite canned gesture classifier model file.
        custom_gesture_classifier_options: Custom gesture classifier options to
          write custom gesture classifier metadata into the TFLite file.
  
      Returns:
        An MetadataWrite object.
      """
      writer = metadata_writer.MetadataWriter.create(
          custom_gesture_classifier_options.model_buffer
      )
      writer.add_general_info(_MODEL_NAME, _MODEL_DESCRIPTION)
      writer.add_feature_input(
          name=_INPUT_NAME, description=_INPUT_DESCRIPTION
      )
      writer.add_classification_output(
          labels=custom_gesture_classifier_options.labels,
          score_thresholding=custom_gesture_classifier_options.score_thresholding,
          name=_OUTPUT_NAME,
          description=_OUTPUT_DESCRIPTION,
      )
      return cls(
          hand_detector_model_buffer,
          hand_landmarks_detector_model_buffer,
          gesture_embedder_model_buffer,
          canned_gesture_classifier_model_buffer,
          writer,
      )
  
file_path: mediapipe/mediapipe/model_maker/python/vision/gesture_recognizer/metadata_writer.py
imports: import dataclasses
  import os
  import tempfile
  from typing import Union
  import tensorflow
  from mediapipe.tasks.python.metadata.metadata_writers import metadata_writer
  from mediapipe.tasks.python.metadata.metadata_writers import model_asset_bundle_utils
label_desc: Use image node_id 3 for calling the MetadataWriter obj's underlying member method code with example usage: obj.create(cls, hand_detector_model_buffer, hand_landmarks_detector_model_buffer, gesture_embedder_model_buffer, canned_gesture_classifier_model_buffer, custom_gesture_classifier_options) and returns: cls